The Emerging Role of Digital Diagnosticsin Precision Medicine Harnessing data to transform patient care

Denise Heaney, PhDSenior Scientific Affairs Manager, Diagnostics Information SolutionsRoche Diagnostics Corporation

Healthcare Sustainability

A growing global concern

Aging population

Declining reimbursement

Chronic disease

Skilled workforce

Quality standards & complexity

Health systems are facing pressure to do more withless

© Roche 2019

The need for a sustainable solutionLeveraging data and insights to achieve healthcare's triple aim

Source: Institute of Healthcare Improvements

http://www.ihi.org/Engage/Initiatives/TripleAim/Pages/default.aspx; accessed 3/5/2019

Decreaseper capita cost

© Roche 2019

Improve the health of populations

ImprovePatient

Experience

The power of data and technology to enablethe transformation

from volume to value-based healthcare.

Information OverloadExponential growth in medical knowledge is overwhelming cognition

By 2020, medical knowledge will double every 2 months !

“[The average oncologist] would have to read 29hrs each work day to stay abreast of new

insights… and despite the availability of pathways and guidelines, compliance with evidence is

extremely low.”

7 years

1950

1980

2010

50 years

© Roche 2019

3.5 years

ASCO February 2018Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116346/; accessed 1/22/2019

Changes in Healthcare Environment

Fundamental changes in science & medicine…

Increased understanding of DISEASE BIOLOGY

Advances in DISEASE CHARACTERIZATION

and MOLECULAR INFORMATION

Novel DIAGNOSTIC TECHNOLOGIES

enter clinic

S E Q U E N C I N GS C I E N C E M O L E C U L A R

6

Changes in Healthcare Environment

…and fundamental changes in data & IT

I N T E G R A T I O N & A N A LY T I C S

PAY E R SR E A L W O R L DD A TA

New and alternativeDATA SOURCES

available at scale

Initiation of

DATA INTEGRATION AND ANALYTICS

Increased focus on OUTCOME and VALUE-

BASED CARE

7

Challenges - Increasing Complexity

Layers of interdependent data adds complexity at various levels

Multi-disciplinary XRAYS &/or LDCT SCANS >8mm LUNG MASS

Work Up

BIOPSY/Surgical Excision

BENIGN H&E (HE600)Diagnosis

MALIGNANT

SERUM PRO GRP/NSE

Classification

ADENO CA SQ CA MESOTH SCL CA METASTATIC CA TTF1+ NAPA+ CK5/6, P40 EPCAM, CAIR CD56, CHRA, SYN CK, LCA

Mechanism of IHC PCR NGS Disease / Therapy

Targets

ALK COBAS EGFR V2 77 TARGET GENES

Host Response / IHC NGS

Targets of Immunotherapy

PDL-1 CD4/8/FOXP3 IMMUNE REPERTOIRE SEQUENCING

Monitoring ctDNA 77 GENES (NCCN) PCR COBAS EGFRV2 (PLASMA)

PractitionerPatient

Surgeon Radiologist Pathologist Molecular

Biologist

Radio-

therapist

Oncologist /Hematologist

Other Specialists

Diagnostic Complexity

Wo

rkflow

Co

mp

lexity

Tre

atm

en

tC

om

ple

xity

8Disease Complexity

Personalized Healthcare

Next wave of Personalized Healthcare coming within reach

Individuals

Broad segments

Patient scope

Specific groups

2020+2000/2010suntil 1990s

Individualized Treatments

Targeted Medicines

Blockbuster Drugs

9

Opportunity – Leverage Healthcare DataMore data will be captured and stored than ever before

Healthcare data1

Health & fitness trackers

tripled from 26m in 2014 to

87m in 2017

U.S. EMR adoption

in oncology clinics has

increased from ~10% to >95%2

Healthcare EMR Providers

aggregating rich data from

EMRs (single providers

believed to have up to 50

petabytes) (including images

and annotations3)

150 Exabytes

2,300 Exabytes

1 International Data Corporation, US only; 2 ONC/American Hospital Association (AHA), AHA Annual Survey Information Technology Supplement 3 Big data analytics in healthcare: promise and potential (Raghupathi and Raghupathi);

Meaningful data AT SCALE

High quality data with a purpose: to answer key scientific questions

Covering enough patients to be generalizable

Advanced Analytics to derive new insights from MDAS

Opportunity Of Digitalization in Healthcare

Data-driven insights improve patient care & enable R&D

14

Create Insights Realize ValueAccess Meaningful

Data at Scale (MDAS)

Advanced analytics of integrated data & decision support

Drive more effective and efficient R&D

Improve access & personalized patient care

Feedback & learning

Diagnostic / molecular data

Clinical trial data

Longitudinal Real-world data

Clinical Decision Support For Precision Medicine

Turning insights into action

2017 Roadmap for Innovation – ACC Health Policy Statement. J Am Coll Cardiol 2017;70:2696–718.

Opportunities for decision support

Complexity of data and disease requires Decision Support

Growing complexity of DISEASE

Gro

win

g co

mp

lexi

ty o

f D

ATA

POTENTIAL FUTURE FOCUS

Neurology

Immunology/ID

Cardiology

CURRENT FOCUS

Oncology

Respiratory

Diabetes

Coagulation

OTHER AREAS OF POTENTIAL FOCUS

Digital Transformation

Clinical Decision Support supplements physician knowledge

17

Antibiotic susceptibility testing

Data, Analytics & Decision Support – Examples Overview

Transforming data into actionable insights

Digitally-Augmented DiagnosticsR&D Clinical Workflow

HIV monitoring Workflow solutions formultidisciplinary care teams

AccessAccess

The Cancer Patient Journey

A complex workflow with multiple stakeholders

12

Pre-Diagnosis Diagnosis Decision Treatment Survivorship

Primary Care

Pathol Radiol

Organ Specialist

Screening

Emergency

Labs

Image

Tumor Board

Decision-maker

Nurse

Specialist

Clinical Specialist

Nurse

Specialist

Clinical Specialist

Admin = PAS Labs = LIS Images = PACS Clinical = Notesor EMR

Labs

Image

Primary Care

Recurrence

Remission

Decision time

The tumor board – potentially the most important 3 minutes in cancercare

Tumor boards provide a multidisciplinary approach to cancer treatment planning. (National Cancer Institute, 2012)

Accepted and established part of the care of cancer patients for decades. (Keating et al, 2013)

96% of respondents to an ASCO survey reported tumor boards to be beneficial for patients and of high-educational value (El-Saghir et al, 2015)

20

Tumor Boards…the evidence

Improving the quality of care and outcomes for cancer patients

Systematic literature review (Pillay et al, 2015)

27 studies

4-45% had their diagnostic report changed.

Patients were more likely to receive more accurate and complete pre-operative staging

Retrospective longitudinal national cohort study (Pan et al, 2015)

MDT care significantly associated with higher survival rate of patients with stage III and IV NSCLC

21

Tumor Boards…the evidence

Or maybe not improving quality and outcomes?

Systematic Literature Review (Taplin et al, 2015)

56 studies

MDTs improve therapy planning, adherence to recommended preoperative assessment, pain control, and adherence to medications.

No convincing evidence that MDTs affect patient survival orcost of care, or studies of how or which MDT processes andstructures were associated with success.

Survey study (Keating et al, 2012)

136 VA medical centers

Observed little association of multidisciplinary tumor boards with measures of use, quality, or survival

22

Tumor Boards…the evidence

Factors that impact on decision-making and decision implementation

Impact on decision-making

Inadequate clinical information Lack of investigation results

Non-attendance of key membersTeleconferencing failure

23

Impact on decision Implementation

Non-consideration of patients’ choices or co-morbidities

Disease progression at the time of implementation

Tumor Boards…the challenges

How to achieve positive outcomes

17

Good format and Good preparation and Qualified and effective Efficient leadership and

structure of the meeting selection of cases faculty interactions betweenphysicians present

Information Factors

Human Factors

1. Ensure access to the right data at the right time

2. Improve the ease of preparation for tumor board

3. Improve & standardize conduction of tumor board

1. User-centered design methodology

2. Clinicians/Hospitals as development partners

3. Goal to augment, not change, clinical workflow

Digitization of Tumor Board information

A workflow solution for Oncology care teams

19

19

Clinical Workflow Solutions

Optimizing workflow for multidisciplinary care teams

Document DecisionsConduct MeetingPrepare For MeetingCoordinate LogisticsCollect Patient Data

Applications in infectious diseases, cardiology and oncology

1 http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2018;volume=9;issue=1;spage=26;epage=26;aulast=krupinski

Use case / need

Time-consuming process Lack of standardization Lack of specialized solution

Example oncology

Oncologists spent 53% & radiologists 12% less time in tumor board meeting preparation, according to new study results,

published in Journal of Pathology Informatics.1

Tumor Board Software Solution

More of the pathologists time was spent reviewing pathology reports

Result

Pathologists performed less non-pathology related tasks.

And…..

The surgeon, oncologist & radiologist allspent more time reviewing the pathologyreports prior to the tumor board

27

Clinical Workflow Solutions

Advancing Clinical Decision Support for Oncology care teams

NAVIFY.com

28

Decision Support

Decision support connecting bench and bedside – via data

8

Clinical Decision Support

ResearchDecision Support

Evidence-basedtreatment options outlined

New R&D hypotheses generated

Potential biomarkersselected

Patients matched to clinical trials

Clinical trial datagenerated

A B

Comprehensive diagnosis performed

Treatment planselected

Real world data1

generated

C D

Database &Analytics

1 electronic medical records, registries, diagnostic data incl. sequencing, a.o.

Smarter, more informative R&D

Improved access & personalized patient care

Clinical Decision Support Closing Gaps

Providing solutions along entire patient journey

30

Diagnosis Treatment

Clinicaltrials

Established therapy option

Treatment decisions

Clinical Decision Support

Monitoring / survivorship

Doing now what patients need next